An electric power system includes an energy management system configured to control a generated power output level of one or more electric generator resources, and a configuration of two or more high-voltage transmission lines, for efficient operation based, at least in part, on a topology error estimation using binary values corresponding to switch statuses. Increased accuracy of network topology estimates facilitates improved state estimation, which in turn enables production of more highly optimized control of electric generator resources and configuration of high-voltage transmission lines.

The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

A computer-implemented method and system is provided. The system manipulates load curves corresponding to time-variant energy demand and consumption of a built environment. The system analyzes a first, second, third, fourth and a fifth set of data. The first set of data is associated with energy consuming devices. The second set of data is associated with an occupancy behavior of users. The third set of data is associated with energy storage and supply means. The fourth set of data is associated with environmental sensors. The fifth set of data is associated with energy pricing models. The system executes control routines for controlling peak loading conditions associated with the built environment. The system manipulates an optimized operating state of the energy consuming devices. The system integrates the energy storage and supply means for optimal reduction of the peak level of energy demand concentrated over the limited period of time.

The present disclosure provides a computer-implemented method for prioritizing one or more instructional control strategies to reduce time-variant energy demand of a built environment associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes parsing and comparison of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes identification and prioritization of one or more instructional control strategies to reduce the time-variant energy demand associated with the built environment.

A management system includes a management apparatus; a power conversion apparatus configured to convert, at least one of output power from a distributed power source and input power into the distributed power source, into AC power or DC power; and a display apparatus configured to display a state of the power conversion apparatus. The management apparatus includes a transmitter configured to transmit to the display apparatus, a transmission source message for specifying a transmission source of a power control message controlling the power conversion apparatus.

Disclosed is a method for identifying the topology of an electric power network allows for the automatic and efficient identification of network topology without the knowledge of network parameters. The method is based on the estimation of mutual current sensitivity coefficients and on an algorithm to obtain the network incidence matrix from the estimated sensitivity coefficients. This algorithm is based on the general assumption that the metering unit that is the most sensitive to the variations of the measured current in a branch connected to a particular node is the metering unit that is arranged at the physical node immediately upstream form the particular node. The method effectively considers the presence of noise in the measurements and its time correlation.

In each of battery modules, a processing circuit manages a battery unit connected to a power line. A communication circuit communicates data to be transmitted or received by the processing circuit. An isolated circuit insulates in a direct current between a first terminal of the communication circuit and a positive-electrode terminal or a negative-electrode terminal of the battery unit, and between a second terminal of the communication circuit and a conductive body to be a common potential of a plurality of the battery modules except for the power line.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

In a power-supply device, a FET passes or blocks a current flowing from one side in a power-supply circuit. A current sensor detects a current flowing into the FET. A FET is coupled to the FET, and passes or blocks a current flowing from another side in the power-supply circuit. A current sensor detects a current flowing into the FET. A junction couples a load unit at a point between the FET and the FET. In each of switch units, a CPU controls the corresponding switch unit of the power-supply circuit based on the detection result detected by the corresponding current sensors.

A power distributor having a first controller and having a number of connection modules which are connected thereto and are intended to be mounted on a supporting rail. The connection module has a number of lateral coupling contacts for the electrical and/or signalling contact connection to at least one further connection module which is attached or can be attached to the particular connection module, wherein a base resistor is connected in each case between two coupling contacts inside the module in such a manner that the base resistors of the attached connection modules form a resistor chain which is connected in series. The connection modules are each equipped or can be equipped with an actuatable circuit breaker. The circuit breakers have a second controller inside the circuit breaker for recording a voltage dropped across the respectively associated base resistor.